Goode Homolosine Projection (equal area - often used in Rand McNally atlas)
Source: https://pubs.er.usgs.gov/publication/pp1453
This week’s assignment will encompass the following concepts covered in Class 6 lecture & lab:
Specific techniques covered this week will include:
Note: the lecture recording is for those students that missed class 6, Monday 10th; and further, students that want to review slides that were skipped during Monday’s lecture. Take note that the lecture is from a previous semester and is marked ‘lecture 5’, but covers the class 6 material - Map Projections.
This week’s readings will include 1 section from the Essentials of Geographic Information Systems textbook; further, the supplemental technical readings cover best approaches to choosing and using map projections.
The class 6 quiz will cover only content from the Essentials of Geographic Information Systems textbook as follows:
Class 6 Supplemental Readings
Readings:
In this assignment, you will create 1 map deliverable, but the assignment will be divided into two components - first a projection exercise for UTM; second, the mapping product deliverable itself.
The map delieverable will include the reprojection of vector features
derived from OpenStreetMap.
CRS parameters for WGS84 will be transformed to an
appropriate UTM Zone for an global city. In this way, the ‘shape’ of the
city and accurate areal measurements will be gained. The dominating land
use type will be offset from other land use types. A plugin tool
Group Stats will be used to sum multiple polygons for land
use totals.
UTM Zones
Source: http://earth-info.nga.mil/GandG/coordsys/grids/universal_grid_system.html
Utilize Task
5 - Exploring the Universal Transverse Mercator (UTM) Coordinate
System to gain an understanding of the UTM map
projection system.
First, ‘set’ the UTM projection. Utilize the zone
that contains your current location. As default, NYC is located in Zone
18:
Project CRS Properties - Initial Search for correct UTM Zone
UTM projection will be visible as slightly
curved grid, similar to the following image:UTM Overlay to Land Features
Note: Task 5 utilizes
UTMzones based on theNAD83datum. In Part II, we will utilize theUTMzone system, but will use theWGS84datum. Both are perfectly acceptable.NAD83is used primarily for geographies in North America, whereasWGS84datum is appropriate for geographies worldwide. If your chosen location for Task 5 above is outside the contiguous US, UTM in theWGS84datum is best.
Below note that when the search term is
utm 18, several versions of UTM are returned, includingNAD83andWGS84. Note also that there areSoptions andNoptions. Since UTM runs perpendicular to the equator, any location south of the equator fall in theSoptions and those north fall in theNoptions:
QGIS CRS Filter
To start, we will utilize OSM OpenStreetMap data -
specifically land uses within cities - to understand the value of map
projections for large scale mapping. The OSM data will be
delivered in WGS84 - a Geodetic Coordinate System, not
Projected Coordinate System. In this assignment, the GCS will be
transformed to a PCS - WGS84 to UTM - to gain
more accurate areal representation, and importantly gain a planar map
unit unlike the angular unit of WGS84 - a world geodetic
coordinate system with decimal degrees as its map unit.
Step 1: Navigate to bbbike.org which
features city extracts from the OSM dataset. These extracts
are updated daily and feature 200 cities in total.
bbbike.org City Extracts
.shp zipped
option for chosen city OSM data (Aarhus will be utilized as
the demonstration):OSM Extract Options per City
landuse.shp as the input data for the map
development:Landuse .shp
landuse features to the C6 Asgmt 6 Data -
Part II - UTM Zones. Detect which UTM zone your city
intersects. In the following image, the city Aarhus intersects with zone
32, adjacent to zone 33 to the east:OSM Landuse with UTM zone overlay
UTM zone. Here zone 32 north is selected
upon export of the aarhus_landuse feature layer. First,
right-click > export > save feature as > navigate to
the CRS button to the far right:Select CRS Icon
UTMzone:QGIS CRS Selector
exports folder that you
create. Open a new .qgs and discard the current
.qgs project:Save Vector Layer as… will embed new CRS into the export feature
.qgs and
immediately import the projected feature for landuse. Note the ‘shape’
difference from the unprojected WGS84 instance of the same
landuse features. Here the shape of the data is much more upright. Check
the project CRS in the lower right of the Map Canvas -
it should inherit the properties of the layer and show the EPSG code for
the layer UTM zone:Landuse in QGIS Map Canvas
Field Calculator, derive sq. area. Since
UTM features a map unit in meters, the return column value
will also be in meters (refer to Class 4 & 5 sq. area procedures).
Alternatively, utilize Vector > Geometry Tools > Add Geometry
Attributes:Add Geometry Attributes
UTM will always feature
meters as planar unit, so the square area returned will be sq.
meters:Geometry Attributes can abide by either project or layer CRS
Invalid Geometry Error/Warning
Fix Geometries vis Processing Toolbox
Fix Geometries vis Processing Toolbox
Fix Geometries vis Processing Toolbox
Fixed geometries. The result will be
Added geom info which will now have the necessary sq. areal
unit (sq meters):Resulting Feature with Added Geometry Attributres
Group Stats plugin:Group Stats Plugin Tool
sum of total sq. area (meters). To enact, populate the tool
as follows using the Added geom info temporaray layer as
the input. Note variable population at bottom of tool panel:Group Stats Plugin Tool
Group Stats Plugin Tool
Copy Values to Clipboard
OSM
representation of your city choice:Note: UTM map units are meters thus to derive sq. km the formula is:
km² =m²/1000000
Resulting Predominant Landuse and m²
Step 6: With the total sq. kilometers derived
for the largest land use in your city per the OSM dataset,
this value can be used in your final map design. Keep it saved as
.txt for your cartographic design; its not necessary for
the next process.
Within the Group Stat tool with the largest land use
highlighted, Show selected on map (this creates a selection
in the attribute table of all land use polygons that fit into the
largest land use category):
Select via Group Stat Plugin
Selected Landuse Features
code. Make sure to populate the calculator as
shown:Code the predominant Landuse Features with Code = 1
code with a integer 1
added to only those records that are part of the largest land use type.
All other values result as NULL.Result
NULL values are now
selected. These new selections are all polygons that are not of the type
of the largest land use:Invert Selection
code field and
enter integer 2:Inverted Selection as Code =2
code field. Return to
Map Canvas. Toogle OFF editing and save the
edits. Deselect all features so there are no yellow hightlights in the
feature layer. Next, export Added geom info as a
.shp titled city_name_landuse.shp:Save Feature with the new UTM CRS
code variable is now ready
to map, resulting in a categorical thematic map for the largest land use
type vs all other land uses in the selected city:Categorical Symbolization on Code Integer Value
Save the project, and pivot to the cartographic design of the final map. See the map examples for general map layout expectations. Include the following:
Legend designed for the categorical land use types - the dominant land use vs all other land uses
Map titling
Data attribution and author tag
(data from OpenStreetMap)
Thematic map design corresponding to the map legend
Final Note:
As OpenStreetMap is an open-source, community effort - not
designed exclusively for GIS analysis - there are typically more
‘mistakes’ in the dataset than data published by public agencies and
private companies/corporations. If you see obviously incorrect map
features, you may want to remove those features before running
Group Stat and deriving final mapping outputs. For
instance, there is a very long and narrow polygon within the Aarhus data
that should probably be removed as its likely a mistake. It does not
appear to follow the larger pattern of the city, and it interrupts many
polygons unnaturally. To remove a feature, first choose a selection mode
from the Tool Menu:
Select Features Tool
Selected Feature - narrow, elongated features in OSM are often mistakes in the dataset
Move selection to top:Selection Moved to Top of Attribute Table
Toogle editing mode ON:Toogle Edit Mode
Delete selected features:Selected Feature Deleted
‘cleaned’ Resulting Feature
print composer
map page:Utilize the following code and steps to insert a layer CRS text into
final map design in print composer.
Create a custom function in text item properties:
Function Editor tab: Code:from qgis.core import *
from qgis.gui import *
@qgsfunction(args='auto', group='Custom')
def get_crs(layer_name, feature, parent):
return QgsProject.instance().mapLayersByName(layer_name)[0].crs().description()
Click on “Load”
In the the Expression tab, type:
get_crs( 'your_layer_name' )
Note: If this optional step proves cumbersome, the CRS can simply be placed as a text item into the final map layout. The advantage of utilizing functions in QGIS layout is reproducible results that are coded into the layout document. This optional step in the assignment allows you to practice this capacity in QGIS layout design.
Print Composer:Deliverable: Produce the assignment map at 11”x17” PDF. Make sure to produce legend items, source tag and titling. No need for north arrow; however, a scale bar can be an effective for this mapping. Upload PDF map export to the assignment location for Class 6 Assignment.
Guide for working with Map Projections in QGIS REFERENCE GUIDE
The IOGP’s EPSG Geodetic Parameter Dataset is a collection of definitions of coordinate reference systems and coordinate transformations which may be global, regional, national or local in application.
.shpof the dataset is available HERE.
This dataset can be useful to overlay to project data in preparation of
necessary geographic datum transformations.EPSG Geodetic Parameter Dataset
Interesting Article on the Dymaxion Map Projection: Reinterpreting Bucky Fuller’s Dymaxion Map